FIG. 8 is a view illustrating a first example of the related art. In the figure, reference numeral 1 indicates an active noise reduction system. As illustrated, it is comprised of a sensor microphone 2, a digital filter 3, a least mean square (LMS) algorithm 4, a finite impulse response (FIR) filter 5, a control use speaker 6, and an error microphone 7.
The active noise reduction system 1 according to this first example of the related art is an active noise reduction system exhibiting a high noise reduction performance for noise applied from a specific direction (for example, see the later-mentioned Patent Document 1). As illustrated in this figure, the sensor microphone 2 set in the noise arrival direction detects a signal expressing the generated noise (reference signal) and outputs a control sound generated by this reference signal from the control use speaker 6.
Further, the error microphone 7 is used, set, at the noise reduction area where it is desired to reduce the noise, so as to detect residual noise e(t) after interference between the control sound and the noise and adaptively find the filter coefficient of the FIR filter 5 which generates a control signal y(t) from the above reference signal so that this residual noise e(t) becomes the smallest. This makes it possible to realize stable noise reduction performance while tracking changes in the microphones 2 and 7 or speaker 6 over time and even changes in the temperature or humidity in the spatial transfer system. The active noise reduction system 1 having configuration, as is the above first example, is called a “feedforward type ANC”.
As the adaptive algorithm (4) used here, up until now, the LMS, recursive least squares (RLS), and numerous other algorithms have been proposed. However, it is necessary to generate the control sound from the speaker 6 in real time, so the filtered-x LMS (least mean square) algorithm which requires the small amount of processing is frequently used (see the later-mentioned Non-Patent Document 1). The basic principle is to update the filter coefficient of the FIR filter 5 based on the “fastest reduction method” so that the residual noise e(t) is reduced while taking into consideration the transfer characteristics of the sound which travels from the control use speaker 6 to the error microphone 7.
As illustrated in the figure, the reference signal at the time t is designated as x(t). This reference signal x(t) is converted to a vector to obtain a vectored x(t)=[x(t), x(t−1), . . . , x(t−Nw+1)]. The following transfer function expressing the transfer characteristic between the control use speaker 6 and the error microphone 7ŵ=[ŵ(1),ŵ(2), . . . , ŵ(Nw)]where Nw is the tap number of the filter.
The above /w is convoluted to obtain the signal (filtered reference signal) r(t) expressed by the following formula (1):r(t)=ŵ*x(t)  (1)where, * indicates convolution of the vector.
The update formula of the filter coefficient of the FIR filter 5 can be converted to the following formula (2) using r(t)=[r(t), r(t−1), . . . , r(t−Nh+1)] for converting the signal of formula (1) to a vector.h(t+1)=h(t)+μ·e(t)·r(t)  (2)
where, e(t) is the residual noise signal at the time t, and
μ is the parameter of the step size and, further,
h(t)=[h(1, t), h(2, t), . . . , h(Nh,t)] expresses the filter coefficient of the adaptive filter at the time t.
However, if trying to effectively reduce noise in a noise reduction area even for various noise arriving from various directions, it would be necessary to prepare a plurality of the above active noise reduction systems 1. In this regard, if trying to simultaneously reduce noise arriving from a plurality of directions, the inconvenience arises that convergence of filter coefficients could no longer be guaranteed due to the interference among the control use speakers 6. Further, if trying to take even that mutual interference into consideration, the amount of processing of the CPU would further increase and therefore real time noise reduction would become difficult.
To deal with this inconvenience, there has been proposed the active noise reduction system 1 of a second example of the related art illustrated in FIG. 9. Note that throughout the drawings, similar components are assigned the same reference numerals or symbols.
The characterizing feature of the second example of the related art illustrated in FIG. 9, as clear from a comparison with the first example of the related art, is, first, to obtain a high noise reduction effect for noise arriving from various directions by arranging a plurality of sensor microphones 2-1, 2-2 . . . 2-n around the noise reduction area where it is desired to reduce the noise so as to detect a plurality of reference signals. Further, the characterizing feature is to select the single reference signal with the highest noise reduction effect among the detected plurality of reference signals at a selector 8 and output it to the speaker 6 side.
In this case, to select the single reference signal with the highest noise reduction effect, a correlation calculating unit 9 is introduced. This correlation calculating unit 9 finds the correlation between the reference signals and calculates the correlation values. The single reference signal with the largest correlation value is selected by the selector 8 (For example, see the following Patent Document 2).    Patent Document 1: Japanese Patent No. 2872545    Patent Document 2: Japanese Patent No. 2921232    Non-Patent Document 1: B. Widrow and S. Stearns, Adaptive Signal Processing (Prentice-Hall, Englewood Cliffs, N.J., 1985)
The active noise reduction system 1 of the second example of the related art (FIG. 9) selects the single reference signal used for generation of the control sound from the speaker 6 based on the correlations of the reference signals obtained by the sensor microphones (2-1 to 2-n). Therefore, the residual noise from the error microphone 7 is not taken into consideration at all, and thus the selected optimal reference signal is not a signal which makes this residual noise minimum. Further, the effects on the analog path (secondary path) included in the residual noise such as (i) the transfer characteristics of the control use speaker 6 and error microphone 7 and (ii) the spatial transfer characteristics in the noise reduction area from the control use speaker 6 to the error microphone 7 are not considered. Therefore, there is the issue that it is difficult to select a reference signal which is stable in terms of the changes in the above transfer characteristics on the analog path.